(p. 16) Back in her office, . . . [rheumatologist Jennifer Frankovich] found that the scientific literature had no studies on patients like this to guide her. So she did something unusual: She searched a database of all the lupus patients the hospital had seen over the previous five years, singling out those whose symptoms matched her patient’s, and ran an analysis to see whether they had developed blood clots. “I did some very simple statistics and brought the data to everybody that I had met with that morning,” she says. The change in attitude was striking. “It was very clear, based on the database, that she could be at an increased risk for a clot.”
The girl was given the drug, and she did not develop a clot. “At the end of the day, we don’t know whether it was the right decision,” says Chris Longhurst, a pediatrician and the chief medical information officer at Stanford Children’s Health, who is a colleague of Frankovich’s. But they felt that it was the best they could do with the limited information they had.
A large, costly and time-consuming clinical trial with proper controls might someday prove Frankovich’s hypothesis correct. But large, costly and time-consuming clinical trials are rarely carried out for uncommon complications of this sort. In the absence of such focused research, doctors and scientists are increasingly dipping into enormous troves of data that already exist — namely the aggregated medical records of thousands or even millions of patients to uncover patterns that might help steer care.
. . .
(p. 17) . . . , developing a “learning health system” — one that can incorporate lessons from its own activities in real time — remains tantalizing to researchers. Stefan Thurner, a professor of complexity studies at the Medical University of Vienna, and his researcher, Peter Klimek, are working with a database of millions of people’s health-insurance claims, building networks of relationships among diseases. As they fill in the network with known connections and new ones mined from the data, Thurner and Klimek hope to be able to predict the health of individuals or of a population over time. On the clinical side, Longhurst has been advocating for a button in electronic medical-record software that would allow doctors to run automated searches for patients like theirs when no other sources of information are available.
With time, and with some crucial refinements, this kind of medicine may eventually become mainstream. Frankovich recalls a conversation with an older colleague. “She told me, ‘Research this decade benefits the next decade,’ ” Frankovich says. “That was how it was. But I feel like it doesn’t have to be that way anymore.”
For the full story, see:
VERONIQUE GREENWOOD. “Eureka; Dr. DATA; Can Statistical Analysis Tell Us What Clinical Trials Cannot?” The New York Times Magazine (Sun., OCT. 5, 2014): 16-17.
(Note: ellipses, and bracketed name, added.)
(Note: the online version of the story has the date OCT. 3, 2014, and has the title “Eureka; Can Big Data Tell Us What Clinical Trials Don’t?”)